This is an Exploratory Data Analysis on the popular TV show Shark Tank India. I got the dataset from kaggle. The dataset consits of information such as the portfolio of different sharks, states from where the pitchers have come, gross margin and valuation offered to the startups.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
from babel.numbers import format_currency
from wordcloud import WordCloud, STOPWORDS
import plotly.express as px
import plotly.io as pio
pio.templates.default = "plotly_dark"
pio.renderers.default = 'notebook'
shark_tank = pd.read_csv("C:/Users/CHIRA/Downloads/Shark Tank India.csv")
shark_tank.sample(10)
| Season Number | Episode Number | Episode Title | Pitch Number | Startup Name | Industry | Business Description | Company Website | Number of Presenters | Male Presenters | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Ghazal Investment Amount | Ghazal Investment Equity | Ghazal Debt Amount | Number of sharks in deal | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 17 | 1 | 6 | New Week, New Ideas | 18 | Hecoll | Beauty/Fashion | Pollution Resistant Fabric - Healthy Cover For... | https://hecoll.com/ | 1 | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 81 | 1 | 26 | Revolutionary Ideas | 82 | Isak Fragrances | Beauty/Fashion | Perfumes Fragrances | https://isakfragrances.com/ | 1 | NaN | ... | NaN | NaN | NaN | 50.0 | 50.0 | NaN | 0.0 | 0.0 | NaN | 1.0 |
| 113 | 1 | 34 | Scaling Ambitions | 114 | On2Cook | Food | Fastest Cooking Device | https://on2cook.com/ | 1 | 1.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 84 | 1 | 27 | Investing In The Future Of India | 85 | Theka Coffee | Food | Coffee Products | NaN | 1 | 1.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 13 | 1 | 5 | Hunt For Interesting Business | 14 | Hungry Head | Food | Restaurant serving 80 types of Maggi | NaN | 2 | 2.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 30 | 1 | 11 | Investment Paane Ka Sapna | 31 | Gopal's 56 | Food | Fiber Ice Cream | https://www.gopals56.in/ | 1 | 1.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 69 | 1 | 22 | Turning Ideas Into Businesses | 70 | Moonshine | Food | Meads | https://www.moonshinemeadery.com/ | 2 | 2.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 46 | 1 | 16 | Winning The Sharks Trust | 47 | Flying Fur | Animal/Pets | Dog Hygiene | https://flyingfur.in/ | 3 | 2.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 49 | 1 | 17 | A Wave Of Startups | 50 | Find Your Kicks India | Beauty/Fashion | Sneaker Resale | https://findyourkicksindia.co/ | 3 | 3.0 | ... | 10.0 | 5.0 | NaN | 10.0 | 5.0 | NaN | NaN | NaN | NaN | 5.0 |
| 109 | 1 | 33 | Life-Changing Ideas | 110 | Proxgy | Technology | VR | https://www.proxgy.com/ | 2 | 2.0 | ... | 0.0 | 0.0 | NaN | 50.0 | 5.0 | NaN | NaN | NaN | NaN | 2.0 |
10 rows × 50 columns
shark_tank.shape
(121, 50)
shark_tank.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 121 entries, 0 to 120 Data columns (total 50 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Season Number 121 non-null int64 1 Episode Number 121 non-null int64 2 Episode Title 121 non-null object 3 Pitch Number 121 non-null int64 4 Startup Name 121 non-null object 5 Industry 121 non-null object 6 Business Description 121 non-null object 7 Company Website 112 non-null object 8 Number of Presenters 121 non-null int64 9 Male Presenters 102 non-null float64 10 Female Presenters 62 non-null float64 11 Couple Presenters 120 non-null float64 12 Pitchers Average Age 121 non-null object 13 Started in 95 non-null float64 14 Pitchers City 114 non-null object 15 Pitchers State 116 non-null object 16 Yearly Revenue 52 non-null float64 17 Monthly Sales 65 non-null float64 18 Gross Margin 35 non-null float64 19 Original Ask Amount 121 non-null float64 20 Original Ask Equity 121 non-null float64 21 Valuation Requested 121 non-null int64 22 Received Offer 121 non-null int64 23 Accepted Offer 88 non-null float64 24 Total Deal Amount 67 non-null float64 25 Total Deal Equity 67 non-null float64 26 Total Deal Debt 9 non-null float64 27 Valuation Offered 67 non-null float64 28 Ashneer Investment Amount 54 non-null float64 29 Ashneer Investment Equity 54 non-null float64 30 Ashneer Debt Amount 2 non-null float64 31 Namita Investment Amount 62 non-null float64 32 Namita Investment Equity 62 non-null float64 33 Namita Debt Amount 1 non-null float64 34 Anupam Investment Amount 67 non-null float64 35 Anupam Investment Equity 67 non-null float64 36 Anupam Debt Amount 1 non-null float64 37 Vineeta Investment Amount 34 non-null float64 38 Vineeta Investment Equity 34 non-null float64 39 Vineeta Debt Amount 1 non-null float64 40 Aman Investment Amount 56 non-null float64 41 Aman Investment Equity 56 non-null float64 42 Aman Debt Amount 1 non-null float64 43 Peyush Investment Amount 53 non-null float64 44 Peyush Investment Equity 53 non-null float64 45 Peyush Debt Amount 5 non-null float64 46 Ghazal Investment Amount 13 non-null float64 47 Ghazal Investment Equity 13 non-null float64 48 Ghazal Debt Amount 0 non-null float64 49 Number of sharks in deal 67 non-null float64 dtypes: float64(36), int64(6), object(8) memory usage: 47.4+ KB
shark_tank.describe()
| Season Number | Episode Number | Pitch Number | Number of Presenters | Male Presenters | Female Presenters | Couple Presenters | Started in | Yearly Revenue | Monthly Sales | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Ghazal Investment Amount | Ghazal Investment Equity | Ghazal Debt Amount | Number of sharks in deal | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 121.0 | 121.000000 | 121.000000 | 121.000000 | 102.000000 | 62.000000 | 120.000000 | 95.000000 | 52.000000 | 6.500000e+01 | ... | 56.000000 | 56.000000 | 1.0 | 53.000000 | 53.000000 | 5.000000 | 13.000000 | 13.000000 | 0.0 | 67.000000 |
| mean | 1.0 | 19.305785 | 61.000000 | 2.082645 | 1.735294 | 1.209677 | 0.208333 | 2018.052632 | 405.134615 | 1.455049e+06 | ... | 15.973036 | 2.932964 | 50.0 | 14.899061 | 5.996981 | 23.400000 | 9.999250 | 3.592308 | NaN | 2.223881 |
| std | 0.0 | 10.375326 | 35.073732 | 0.927243 | 0.974186 | 0.483739 | 0.407819 | 2.481285 | 1055.349288 | 3.167937e+06 | ... | 22.947765 | 5.810317 | NaN | 21.192558 | 13.631118 | 2.302173 | 12.378205 | 5.322352 | NaN | 1.165422 |
| min | 1.0 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.000000 | 2005.000000 | 0.000000 | 0.000000e+00 | ... | 0.000000 | 0.000000 | 50.0 | 0.000000 | 0.000000 | 20.000000 | 0.000000 | 0.000000 | NaN | 1.000000 |
| 25% | 1.0 | 11.000000 | 31.000000 | 1.000000 | 1.000000 | 1.000000 | 0.000000 | 2017.000000 | 53.750000 | 2.000000e+05 | ... | 0.000000 | 0.000000 | 50.0 | 0.000000 | 0.000000 | 22.000000 | 0.000000 | 0.000000 | NaN | 1.000000 |
| 50% | 1.0 | 19.000000 | 61.000000 | 2.000000 | 1.000000 | 1.000000 | 0.000000 | 2019.000000 | 112.500000 | 5.500000e+05 | ... | 3.500008 | 0.875000 | 50.0 | 8.330000 | 1.000000 | 25.000000 | 0.000253 | 1.000000 | NaN | 2.000000 |
| 75% | 1.0 | 28.000000 | 91.000000 | 3.000000 | 2.000000 | 1.000000 | 0.000000 | 2020.000000 | 255.500000 | 1.600000e+06 | ... | 25.000000 | 4.250000 | 50.0 | 25.000000 | 5.000000 | 25.000000 | 20.000000 | 5.000000 | NaN | 3.000000 |
| max | 1.0 | 36.000000 | 121.000000 | 6.000000 | 6.000000 | 3.000000 | 1.000000 | 2022.000000 | 7200.000000 | 2.000000e+07 | ... | 100.000000 | 40.000000 | 50.0 | 100.000000 | 75.000000 | 25.000000 | 33.330000 | 17.500000 | NaN | 5.000000 |
8 rows × 42 columns
shark_tank.columns
Index(['Season Number', 'Episode Number', 'Episode Title', 'Pitch Number',
'Startup Name', 'Industry', 'Business Description', 'Company Website',
'Number of Presenters', 'Male Presenters', 'Female Presenters',
'Couple Presenters', 'Pitchers Average Age', 'Started in',
'Pitchers City', 'Pitchers State', 'Yearly Revenue', 'Monthly Sales',
'Gross Margin', 'Original Ask Amount', 'Original Ask Equity',
'Valuation Requested', 'Received Offer', 'Accepted Offer',
'Total Deal Amount', 'Total Deal Equity', 'Total Deal Debt',
'Valuation Offered', 'Ashneer Investment Amount',
'Ashneer Investment Equity', 'Ashneer Debt Amount',
'Namita Investment Amount', 'Namita Investment Equity',
'Namita Debt Amount', 'Anupam Investment Amount',
'Anupam Investment Equity', 'Anupam Debt Amount',
'Vineeta Investment Amount', 'Vineeta Investment Equity',
'Vineeta Debt Amount', 'Aman Investment Amount',
'Aman Investment Equity', 'Aman Debt Amount',
'Peyush Investment Amount', 'Peyush Investment Equity',
'Peyush Debt Amount', 'Ghazal Investment Amount',
'Ghazal Investment Equity', 'Ghazal Debt Amount',
'Number of sharks in deal'],
dtype='object')
shark_tank.isnull().sum()
Season Number 0 Episode Number 0 Episode Title 0 Pitch Number 0 Startup Name 0 Industry 0 Business Description 0 Company Website 9 Number of Presenters 0 Male Presenters 19 Female Presenters 59 Couple Presenters 1 Pitchers Average Age 0 Started in 26 Pitchers City 7 Pitchers State 5 Yearly Revenue 69 Monthly Sales 56 Gross Margin 86 Original Ask Amount 0 Original Ask Equity 0 Valuation Requested 0 Received Offer 0 Accepted Offer 33 Total Deal Amount 54 Total Deal Equity 54 Total Deal Debt 112 Valuation Offered 54 Ashneer Investment Amount 67 Ashneer Investment Equity 67 Ashneer Debt Amount 119 Namita Investment Amount 59 Namita Investment Equity 59 Namita Debt Amount 120 Anupam Investment Amount 54 Anupam Investment Equity 54 Anupam Debt Amount 120 Vineeta Investment Amount 87 Vineeta Investment Equity 87 Vineeta Debt Amount 120 Aman Investment Amount 65 Aman Investment Equity 65 Aman Debt Amount 120 Peyush Investment Amount 68 Peyush Investment Equity 68 Peyush Debt Amount 116 Ghazal Investment Amount 108 Ghazal Investment Equity 108 Ghazal Debt Amount 121 Number of sharks in deal 54 dtype: int64
Not removing null values here as the null values are not the outliers here, they play an important role in the data. For example, if Ashneer's debt amount is Nan, that means he hasn't given loan to the pitchers and so on.
shark_tank.corr(method = 'pearson').T.round(2).style.background_gradient(cmap='PuBu')
| Season Number | Episode Number | Pitch Number | Number of Presenters | Male Presenters | Female Presenters | Couple Presenters | Started in | Yearly Revenue | Monthly Sales | Gross Margin | Original Ask Amount | Original Ask Equity | Valuation Requested | Received Offer | Accepted Offer | Total Deal Amount | Total Deal Equity | Total Deal Debt | Valuation Offered | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Namita Investment Amount | Namita Investment Equity | Namita Debt Amount | Anupam Investment Amount | Anupam Investment Equity | Anupam Debt Amount | Vineeta Investment Amount | Vineeta Investment Equity | Vineeta Debt Amount | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Ghazal Investment Amount | Ghazal Investment Equity | Ghazal Debt Amount | Number of sharks in deal | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Season Number | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Episode Number | nan | 1.000000 | 1.000000 | -0.010000 | 0.060000 | -0.170000 | -0.070000 | 0.100000 | 0.210000 | -0.020000 | -0.110000 | -0.070000 | -0.100000 | -0.010000 | -0.090000 | -0.230000 | -0.010000 | -0.010000 | 0.140000 | -0.060000 | -0.030000 | -0.040000 | 1.000000 | 0.060000 | 0.080000 | nan | -0.130000 | -0.120000 | nan | -0.030000 | -0.300000 | nan | -0.070000 | -0.030000 | nan | 0.080000 | 0.010000 | 0.040000 | -0.030000 | -0.070000 | nan | 0.110000 |
| Pitch Number | nan | 1.000000 | 1.000000 | -0.010000 | 0.060000 | -0.170000 | -0.060000 | 0.100000 | 0.220000 | -0.020000 | -0.120000 | -0.080000 | -0.110000 | -0.010000 | -0.100000 | -0.240000 | -0.020000 | -0.010000 | 0.150000 | -0.060000 | -0.020000 | -0.020000 | 1.000000 | 0.050000 | 0.070000 | nan | -0.140000 | -0.120000 | nan | -0.040000 | -0.290000 | nan | -0.080000 | -0.040000 | nan | 0.090000 | 0.010000 | 0.030000 | -0.060000 | -0.080000 | nan | 0.110000 |
| Number of Presenters | nan | -0.010000 | -0.010000 | 1.000000 | 0.770000 | 0.400000 | 0.110000 | -0.010000 | 0.020000 | -0.110000 | -0.210000 | -0.110000 | -0.220000 | -0.090000 | 0.090000 | -0.090000 | 0.160000 | -0.340000 | 0.140000 | 0.270000 | 0.040000 | -0.080000 | nan | -0.060000 | -0.110000 | nan | 0.190000 | -0.130000 | nan | 0.160000 | -0.220000 | nan | -0.010000 | 0.060000 | nan | 0.070000 | -0.230000 | 0.460000 | 0.300000 | -0.170000 | nan | 0.180000 |
| Male Presenters | nan | 0.060000 | 0.060000 | 0.770000 | 1.000000 | -0.160000 | -0.360000 | 0.020000 | 0.060000 | -0.160000 | -0.060000 | -0.080000 | -0.210000 | -0.050000 | 0.200000 | 0.000000 | 0.020000 | -0.230000 | 0.370000 | 0.220000 | -0.020000 | -0.060000 | 1.000000 | -0.100000 | -0.220000 | nan | 0.250000 | -0.010000 | nan | -0.050000 | -0.240000 | nan | 0.020000 | 0.060000 | nan | -0.050000 | -0.150000 | -0.040000 | 0.260000 | -0.090000 | nan | 0.120000 |
| Female Presenters | nan | -0.170000 | -0.170000 | 0.400000 | -0.160000 | 1.000000 | -0.090000 | 0.020000 | -0.230000 | -0.160000 | -0.010000 | -0.130000 | 0.090000 | 0.070000 | -0.080000 | -0.160000 | -0.230000 | -0.020000 | nan | -0.180000 | -0.210000 | -0.200000 | nan | -0.190000 | -0.100000 | nan | 0.180000 | 0.090000 | nan | nan | nan | nan | 0.020000 | 0.220000 | nan | -0.150000 | -0.020000 | nan | nan | nan | nan | 0.060000 |
| Couple Presenters | nan | -0.070000 | -0.060000 | 0.110000 | -0.360000 | -0.090000 | 1.000000 | -0.090000 | 0.040000 | 0.140000 | -0.280000 | -0.050000 | -0.060000 | -0.050000 | -0.100000 | -0.080000 | 0.190000 | -0.120000 | 0.020000 | 0.090000 | 0.130000 | -0.070000 | -1.000000 | 0.220000 | 0.280000 | nan | 0.120000 | -0.030000 | nan | -0.030000 | -0.060000 | nan | -0.120000 | -0.130000 | nan | 0.080000 | -0.120000 | 0.390000 | nan | nan | nan | 0.020000 |
| Started in | nan | 0.100000 | 0.100000 | -0.010000 | 0.020000 | 0.020000 | -0.090000 | 1.000000 | -0.040000 | -0.200000 | 0.390000 | -0.170000 | -0.020000 | -0.150000 | 0.170000 | 0.110000 | -0.150000 | -0.040000 | 0.110000 | -0.330000 | -0.040000 | -0.190000 | 1.000000 | 0.020000 | 0.170000 | nan | -0.080000 | 0.160000 | nan | 0.080000 | 0.160000 | nan | -0.160000 | 0.140000 | nan | -0.200000 | -0.230000 | 0.960000 | 0.480000 | 0.660000 | nan | 0.230000 |
| Yearly Revenue | nan | 0.210000 | 0.220000 | 0.020000 | 0.060000 | -0.230000 | 0.040000 | -0.040000 | 1.000000 | 0.270000 | 0.230000 | -0.040000 | -0.200000 | 0.260000 | 0.210000 | -0.360000 | 0.340000 | 0.000000 | nan | 0.410000 | 0.380000 | -0.210000 | nan | -0.220000 | -0.160000 | nan | -0.120000 | -0.140000 | nan | -0.300000 | -0.360000 | nan | 0.480000 | 0.480000 | nan | -0.090000 | -0.090000 | nan | 0.960000 | -0.510000 | nan | -0.270000 |
| Monthly Sales | nan | -0.020000 | -0.020000 | -0.110000 | -0.160000 | -0.160000 | 0.140000 | -0.200000 | 0.270000 | 1.000000 | -0.430000 | 0.080000 | -0.190000 | 0.130000 | 0.120000 | -0.370000 | 0.120000 | -0.490000 | 0.200000 | 0.150000 | 0.180000 | -0.200000 | nan | 0.010000 | -0.190000 | nan | -0.010000 | -0.150000 | nan | -0.330000 | -0.270000 | nan | -0.040000 | -0.210000 | nan | 0.310000 | -0.230000 | 0.930000 | -0.250000 | -0.160000 | nan | -0.140000 |
| Gross Margin | nan | -0.110000 | -0.120000 | -0.210000 | -0.060000 | -0.010000 | -0.280000 | 0.390000 | 0.230000 | -0.430000 | 1.000000 | -0.250000 | 0.270000 | -0.050000 | -0.010000 | 0.450000 | -0.260000 | -0.010000 | nan | -0.230000 | -0.230000 | -0.040000 | nan | 0.360000 | 0.620000 | nan | -0.010000 | 0.020000 | nan | 0.050000 | -0.010000 | nan | -0.340000 | -0.060000 | nan | -0.440000 | -0.560000 | nan | -0.200000 | 0.120000 | nan | 0.010000 |
| Original Ask Amount | nan | -0.070000 | -0.080000 | -0.110000 | -0.080000 | -0.130000 | -0.050000 | -0.170000 | -0.040000 | 0.080000 | -0.250000 | 1.000000 | 0.500000 | 0.860000 | -0.150000 | -0.400000 | 0.610000 | -0.290000 | 0.750000 | 0.420000 | 0.260000 | -0.040000 | 1.000000 | 0.340000 | 0.000000 | nan | -0.060000 | -0.240000 | nan | 0.040000 | -0.340000 | nan | 0.230000 | -0.150000 | nan | 0.320000 | -0.070000 | 0.730000 | 0.350000 | -0.080000 | nan | -0.150000 |
| Original Ask Equity | nan | -0.100000 | -0.110000 | -0.220000 | -0.210000 | 0.090000 | -0.060000 | -0.020000 | -0.200000 | -0.190000 | 0.270000 | 0.500000 | 1.000000 | 0.220000 | -0.260000 | 0.250000 | -0.320000 | 0.730000 | -0.530000 | -0.540000 | -0.210000 | 0.380000 | -1.000000 | -0.060000 | 0.310000 | nan | -0.060000 | 0.260000 | nan | 0.100000 | 0.480000 | nan | -0.260000 | -0.020000 | nan | -0.080000 | 0.430000 | -0.930000 | 0.000000 | 0.260000 | nan | 0.000000 |
| Valuation Requested | nan | -0.010000 | -0.010000 | -0.090000 | -0.050000 | 0.070000 | -0.050000 | -0.150000 | 0.260000 | 0.130000 | -0.050000 | 0.860000 | 0.220000 | 1.000000 | -0.090000 | -0.380000 | 0.400000 | -0.530000 | 0.920000 | 0.740000 | 0.220000 | -0.120000 | 1.000000 | 0.170000 | -0.170000 | nan | 0.060000 | -0.290000 | nan | -0.110000 | -0.350000 | nan | 0.120000 | -0.190000 | nan | 0.370000 | -0.210000 | 0.700000 | -0.140000 | -0.310000 | nan | -0.180000 |
| Received Offer | nan | -0.090000 | -0.100000 | 0.090000 | 0.200000 | -0.080000 | -0.100000 | 0.170000 | 0.210000 | 0.120000 | -0.010000 | -0.150000 | -0.260000 | -0.090000 | 1.000000 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Accepted Offer | nan | -0.230000 | -0.240000 | -0.090000 | 0.000000 | -0.160000 | -0.080000 | 0.110000 | -0.360000 | -0.370000 | 0.450000 | -0.400000 | 0.250000 | -0.380000 | nan | 1.000000 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Total Deal Amount | nan | -0.010000 | -0.020000 | 0.160000 | 0.020000 | -0.230000 | 0.190000 | -0.150000 | 0.340000 | 0.120000 | -0.260000 | 0.610000 | -0.320000 | 0.400000 | nan | nan | 1.000000 | -0.230000 | -0.270000 | 0.500000 | 0.440000 | 0.060000 | -1.000000 | 0.350000 | -0.010000 | nan | 0.140000 | -0.180000 | nan | 0.260000 | -0.280000 | nan | 0.400000 | 0.140000 | nan | 0.430000 | -0.190000 | 0.810000 | 0.510000 | 0.050000 | nan | 0.190000 |
| Total Deal Equity | nan | -0.010000 | -0.010000 | -0.340000 | -0.230000 | -0.020000 | -0.120000 | -0.040000 | 0.000000 | -0.490000 | -0.010000 | -0.290000 | 0.730000 | -0.530000 | nan | nan | -0.230000 | 1.000000 | -0.490000 | -0.520000 | -0.210000 | 0.290000 | -1.000000 | -0.160000 | 0.080000 | nan | -0.040000 | 0.260000 | nan | 0.040000 | 0.360000 | nan | -0.150000 | 0.220000 | nan | 0.050000 | 0.770000 | -0.690000 | -0.000000 | 0.270000 | nan | -0.100000 |
| Total Deal Debt | nan | 0.140000 | 0.150000 | 0.140000 | 0.370000 | nan | 0.020000 | 0.110000 | nan | 0.200000 | nan | 0.750000 | -0.530000 | 0.920000 | nan | nan | -0.270000 | -0.490000 | 1.000000 | -0.080000 | -0.130000 | 0.350000 | 1.000000 | 0.160000 | 0.160000 | nan | -0.130000 | -0.130000 | nan | -0.170000 | -0.170000 | nan | 0.140000 | 0.140000 | nan | -0.490000 | -0.500000 | 0.540000 | nan | nan | nan | 0.020000 |
| Valuation Offered | nan | -0.060000 | -0.060000 | 0.270000 | 0.220000 | -0.180000 | 0.090000 | -0.330000 | 0.410000 | 0.150000 | -0.230000 | 0.420000 | -0.540000 | 0.740000 | nan | nan | 0.500000 | -0.520000 | -0.080000 | 1.000000 | 0.190000 | -0.120000 | -1.000000 | 0.090000 | -0.170000 | nan | 0.250000 | -0.260000 | nan | -0.260000 | -0.370000 | nan | 0.360000 | -0.150000 | nan | 0.290000 | -0.260000 | 0.660000 | -0.090000 | -0.310000 | nan | -0.080000 |
| Ashneer Investment Amount | nan | -0.030000 | -0.020000 | 0.040000 | -0.020000 | -0.210000 | 0.130000 | -0.040000 | 0.380000 | 0.180000 | -0.230000 | 0.260000 | -0.210000 | 0.220000 | nan | nan | 0.440000 | -0.210000 | -0.130000 | 0.190000 | 1.000000 | 0.420000 | -1.000000 | -0.110000 | -0.180000 | nan | -0.120000 | -0.230000 | nan | -0.030000 | -0.130000 | nan | -0.100000 | -0.160000 | nan | 0.250000 | -0.150000 | nan | nan | nan | nan | 0.220000 |
| Ashneer Investment Equity | nan | -0.040000 | -0.020000 | -0.080000 | -0.060000 | -0.200000 | -0.070000 | -0.190000 | -0.210000 | -0.200000 | -0.040000 | -0.040000 | 0.380000 | -0.120000 | nan | nan | 0.060000 | 0.290000 | 0.350000 | -0.120000 | 0.420000 | 1.000000 | -1.000000 | -0.130000 | -0.130000 | nan | -0.130000 | -0.120000 | nan | 0.250000 | 0.610000 | nan | -0.160000 | -0.090000 | nan | 0.040000 | -0.170000 | nan | -1.000000 | -1.000000 | nan | 0.250000 |
| Ashneer Debt Amount | nan | 1.000000 | 1.000000 | nan | 1.000000 | nan | -1.000000 | 1.000000 | nan | nan | nan | 1.000000 | -1.000000 | 1.000000 | nan | nan | -1.000000 | -1.000000 | 1.000000 | -1.000000 | -1.000000 | -1.000000 | 1.000000 | nan | nan | nan | -1.000000 | -1.000000 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | -1.000000 |
| Namita Investment Amount | nan | 0.060000 | 0.050000 | -0.060000 | -0.100000 | -0.190000 | 0.220000 | 0.020000 | -0.220000 | 0.010000 | 0.360000 | 0.340000 | -0.060000 | 0.170000 | nan | nan | 0.350000 | -0.160000 | 0.160000 | 0.090000 | -0.110000 | -0.130000 | nan | 1.000000 | 0.670000 | nan | -0.190000 | -0.270000 | nan | 0.040000 | -0.250000 | nan | -0.080000 | -0.110000 | nan | -0.140000 | -0.240000 | 0.330000 | -0.030000 | -0.150000 | nan | -0.010000 |
| Namita Investment Equity | nan | 0.080000 | 0.070000 | -0.110000 | -0.220000 | -0.100000 | 0.280000 | 0.170000 | -0.160000 | -0.190000 | 0.620000 | 0.000000 | 0.310000 | -0.170000 | nan | nan | -0.010000 | 0.080000 | 0.160000 | -0.170000 | -0.180000 | -0.130000 | nan | 0.670000 | 1.000000 | nan | -0.230000 | -0.220000 | nan | -0.050000 | -0.160000 | nan | -0.160000 | -0.050000 | nan | -0.250000 | -0.210000 | 0.330000 | 0.230000 | 0.100000 | nan | -0.060000 |
| Namita Debt Amount | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Anupam Investment Amount | nan | -0.130000 | -0.140000 | 0.190000 | 0.250000 | 0.180000 | 0.120000 | -0.080000 | -0.120000 | -0.010000 | -0.010000 | -0.060000 | -0.060000 | 0.060000 | nan | nan | 0.140000 | -0.040000 | -0.130000 | 0.250000 | -0.120000 | -0.130000 | -1.000000 | -0.190000 | -0.230000 | nan | 1.000000 | 0.680000 | nan | -0.090000 | -0.050000 | nan | -0.130000 | -0.160000 | nan | -0.080000 | -0.150000 | nan | -0.010000 | 0.090000 | nan | 0.150000 |
| Anupam Investment Equity | nan | -0.120000 | -0.120000 | -0.130000 | -0.010000 | 0.090000 | -0.030000 | 0.160000 | -0.140000 | -0.150000 | 0.020000 | -0.240000 | 0.260000 | -0.290000 | nan | nan | -0.180000 | 0.260000 | -0.130000 | -0.260000 | -0.230000 | -0.120000 | -1.000000 | -0.270000 | -0.220000 | nan | 0.680000 | 1.000000 | nan | 0.010000 | 0.240000 | nan | -0.260000 | -0.100000 | nan | -0.160000 | -0.080000 | nan | 0.040000 | 0.400000 | nan | 0.030000 |
| Anupam Debt Amount | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Vineeta Investment Amount | nan | -0.030000 | -0.040000 | 0.160000 | -0.050000 | nan | -0.030000 | 0.080000 | -0.300000 | -0.330000 | 0.050000 | 0.040000 | 0.100000 | -0.110000 | nan | nan | 0.260000 | 0.040000 | -0.170000 | -0.260000 | -0.030000 | 0.250000 | nan | 0.040000 | -0.050000 | nan | -0.090000 | 0.010000 | nan | 1.000000 | 0.550000 | nan | -0.320000 | -0.080000 | nan | -0.150000 | -0.280000 | nan | 0.800000 | 0.230000 | nan | 0.430000 |
| Vineeta Investment Equity | nan | -0.300000 | -0.290000 | -0.220000 | -0.240000 | nan | -0.060000 | 0.160000 | -0.360000 | -0.270000 | -0.010000 | -0.340000 | 0.480000 | -0.350000 | nan | nan | -0.280000 | 0.360000 | -0.170000 | -0.370000 | -0.130000 | 0.610000 | nan | -0.250000 | -0.160000 | nan | -0.050000 | 0.240000 | nan | 0.550000 | 1.000000 | nan | -0.440000 | -0.250000 | nan | -0.270000 | -0.270000 | nan | 0.420000 | 0.310000 | nan | 0.130000 |
| Vineeta Debt Amount | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Aman Investment Amount | nan | -0.070000 | -0.080000 | -0.010000 | 0.020000 | 0.020000 | -0.120000 | -0.160000 | 0.480000 | -0.040000 | -0.340000 | 0.230000 | -0.260000 | 0.120000 | nan | nan | 0.400000 | -0.150000 | 0.140000 | 0.360000 | -0.100000 | -0.160000 | nan | -0.080000 | -0.160000 | nan | -0.130000 | -0.260000 | nan | -0.320000 | -0.440000 | nan | 1.000000 | 0.650000 | nan | -0.340000 | -0.210000 | nan | -1.000000 | -1.000000 | nan | -0.130000 |
| Aman Investment Equity | nan | -0.030000 | -0.040000 | 0.060000 | 0.060000 | 0.220000 | -0.130000 | 0.140000 | 0.480000 | -0.210000 | -0.060000 | -0.150000 | -0.020000 | -0.190000 | nan | nan | 0.140000 | 0.220000 | 0.140000 | -0.150000 | -0.160000 | -0.090000 | nan | -0.110000 | -0.050000 | nan | -0.160000 | -0.100000 | nan | -0.080000 | -0.250000 | nan | 0.650000 | 1.000000 | nan | -0.270000 | -0.140000 | nan | -1.000000 | -1.000000 | nan | -0.010000 |
| Aman Debt Amount | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Peyush Investment Amount | nan | 0.080000 | 0.090000 | 0.070000 | -0.050000 | -0.150000 | 0.080000 | -0.200000 | -0.090000 | 0.310000 | -0.440000 | 0.320000 | -0.080000 | 0.370000 | nan | nan | 0.430000 | 0.050000 | -0.490000 | 0.290000 | 0.250000 | 0.040000 | nan | -0.140000 | -0.250000 | nan | -0.080000 | -0.160000 | nan | -0.150000 | -0.270000 | nan | -0.340000 | -0.270000 | nan | 1.000000 | 0.340000 | 0.770000 | -0.120000 | -0.250000 | nan | -0.090000 |
| Peyush Investment Equity | nan | 0.010000 | 0.010000 | -0.230000 | -0.150000 | -0.020000 | -0.120000 | -0.230000 | -0.090000 | -0.230000 | -0.560000 | -0.070000 | 0.430000 | -0.210000 | nan | nan | -0.190000 | 0.770000 | -0.500000 | -0.260000 | -0.150000 | -0.170000 | nan | -0.240000 | -0.210000 | nan | -0.150000 | -0.080000 | nan | -0.280000 | -0.270000 | nan | -0.210000 | -0.140000 | nan | 0.340000 | 1.000000 | -0.690000 | -0.310000 | -0.260000 | nan | -0.270000 |
| Peyush Debt Amount | nan | 0.040000 | 0.030000 | 0.460000 | -0.040000 | nan | 0.390000 | 0.960000 | nan | 0.930000 | nan | 0.730000 | -0.930000 | 0.700000 | nan | nan | 0.810000 | -0.690000 | 0.540000 | 0.660000 | nan | nan | nan | 0.330000 | 0.330000 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.770000 | -0.690000 | 1.000000 | nan | nan | nan | 0.390000 |
| Ghazal Investment Amount | nan | -0.030000 | -0.060000 | 0.300000 | 0.260000 | nan | nan | 0.480000 | 0.960000 | -0.250000 | -0.200000 | 0.350000 | 0.000000 | -0.140000 | nan | nan | 0.510000 | -0.000000 | nan | -0.090000 | nan | -1.000000 | nan | -0.030000 | 0.230000 | nan | -0.010000 | 0.040000 | nan | 0.800000 | 0.420000 | nan | -1.000000 | -1.000000 | nan | -0.120000 | -0.310000 | nan | 1.000000 | 0.700000 | nan | 0.390000 |
| Ghazal Investment Equity | nan | -0.070000 | -0.080000 | -0.170000 | -0.090000 | nan | nan | 0.660000 | -0.510000 | -0.160000 | 0.120000 | -0.080000 | 0.260000 | -0.310000 | nan | nan | 0.050000 | 0.270000 | nan | -0.310000 | nan | -1.000000 | nan | -0.150000 | 0.100000 | nan | 0.090000 | 0.400000 | nan | 0.230000 | 0.310000 | nan | -1.000000 | -1.000000 | nan | -0.250000 | -0.260000 | nan | 0.700000 | 1.000000 | nan | 0.130000 |
| Ghazal Debt Amount | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Number of sharks in deal | nan | 0.110000 | 0.110000 | 0.180000 | 0.120000 | 0.060000 | 0.020000 | 0.230000 | -0.270000 | -0.140000 | 0.010000 | -0.150000 | 0.000000 | -0.180000 | nan | nan | 0.190000 | -0.100000 | 0.020000 | -0.080000 | 0.220000 | 0.250000 | -1.000000 | -0.010000 | -0.060000 | nan | 0.150000 | 0.030000 | nan | 0.430000 | 0.130000 | nan | -0.130000 | -0.010000 | nan | -0.090000 | -0.270000 | 0.390000 | 0.390000 | 0.130000 | nan | 1.000000 |
for col in shark_tank.columns:
print("Number of unique values in", col, "-", shark_tank[col].nunique())
Number of unique values in Season Number - 1 Number of unique values in Episode Number - 36 Number of unique values in Episode Title - 36 Number of unique values in Pitch Number - 121 Number of unique values in Startup Name - 121 Number of unique values in Industry - 11 Number of unique values in Business Description - 121 Number of unique values in Company Website - 112 Number of unique values in Number of Presenters - 5 Number of unique values in Male Presenters - 5 Number of unique values in Female Presenters - 3 Number of unique values in Couple Presenters - 2 Number of unique values in Pitchers Average Age - 3 Number of unique values in Started in - 12 Number of unique values in Pitchers City - 40 Number of unique values in Pitchers State - 20 Number of unique values in Yearly Revenue - 39 Number of unique values in Monthly Sales - 41 Number of unique values in Gross Margin - 22 Number of unique values in Original Ask Amount - 24 Number of unique values in Original Ask Equity - 19 Number of unique values in Valuation Requested - 50 Number of unique values in Received Offer - 2 Number of unique values in Accepted Offer - 2 Number of unique values in Total Deal Amount - 21 Number of unique values in Total Deal Equity - 27 Number of unique values in Total Deal Debt - 6 Number of unique values in Valuation Offered - 39 Number of unique values in Ashneer Investment Amount - 10 Number of unique values in Ashneer Investment Equity - 15 Number of unique values in Ashneer Debt Amount - 2 Number of unique values in Namita Investment Amount - 15 Number of unique values in Namita Investment Equity - 18 Number of unique values in Namita Debt Amount - 1 Number of unique values in Anupam Investment Amount - 13 Number of unique values in Anupam Investment Equity - 22 Number of unique values in Anupam Debt Amount - 1 Number of unique values in Vineeta Investment Amount - 10 Number of unique values in Vineeta Investment Equity - 13 Number of unique values in Vineeta Debt Amount - 1 Number of unique values in Aman Investment Amount - 15 Number of unique values in Aman Investment Equity - 20 Number of unique values in Aman Debt Amount - 1 Number of unique values in Peyush Investment Amount - 16 Number of unique values in Peyush Investment Equity - 19 Number of unique values in Peyush Debt Amount - 3 Number of unique values in Ghazal Investment Amount - 7 Number of unique values in Ghazal Investment Equity - 7 Number of unique values in Ghazal Debt Amount - 0 Number of unique values in Number of sharks in deal - 5
print(shark_tank['Season Number'].max(), "season \n")
print(shark_tank['Episode Number'].max(), "episodes \n")
print(shark_tank['Pitch Number'].max(), "startup companies came for pitching \n")
print(shark_tank['Episode Number'].value_counts().sort_values(ascending=True).unique(), "Pitches came per episodes")
1 season 36 episodes 121 startup companies came for pitching [3 4] Pitches came per episodes
Season 1 of Shark tank India was broadcasted on SonyLiv OTT platform.
print(shark_tank['Pitchers City'].value_counts(), "\n")
plt.figure(figsize=(25,10))
plt.xticks(rotation=90)
plt.title("Shark Tank India")
ax = sns.countplot(x = "Pitchers City", data = shark_tank, order = shark_tank['Pitchers City'].value_counts().index)
for i in ax.containers:
ax.bar_label(i,)
Mumbai 21 Delhi 16 Bangalore 10 Pune 9 Ahmedabad 6 Kolkata 5 Hyderabad 5 Gurgaon 3 Noida 3 Surat 2 Gandhinagar 2 Jaipur 2 Jammu 2 Nagpur 2 Mangalore 1 Mathura 1 Varanasi 1 Bhagalpur 1 Lucknow 1 Coimbatore 1 Thane 1 Pune, Delhi 1 Dehradun 1 Malegaon 1 Goa 1 Ludhiana 1 Bangalore, Kolkata 1 Ernakulam 1 Panipat 1 Valsad 1 Indore 1 Chennai 1 Vadodara 1 Thiruvananthapuram 1 Nashik 1 Darbhanga 1 Jalna 1 Mohali,Delhi 1 Baramati 1 Modinagar 1 Name: Pitchers City, dtype: int64
print(shark_tank['Pitchers State'].value_counts())
plt.figure(figsize=(25,10))
plt.xticks(rotation=45)
ax = sns.countplot(x = shark_tank['Pitchers State'], order = shark_tank['Pitchers State'].value_counts().index)
plt.title("Shark Tank India")
for i in ax.containers:
ax.bar_label(i,)
Maharashtra 37 Delhi 16 Gujarat 13 Karnataka 11 Uttar Pradesh 7 Telangana 5 West Bengal 5 Haryana 4 Jammu & Kashmir 2 Rajasthan 2 Tamil Nadu 2 Kerala 2 Bihar 2 Punjab 2 Madhya Pradesh 1 Karnataka, West Bengal 1 Punjab, Delhi 1 Uttarakhand 1 Goa 1 Maharashtra, Delhi 1 Name: Pitchers State, dtype: int64
print(shark_tank['Started in'].value_counts())
plt.figure(figsize=(20,10))
ax = sns.countplot(x = shark_tank['Started in'], order = shark_tank['Started in'].value_counts().index)
for i in ax.containers:
ax.bar_label(i,)
2019.0 22 2020.0 14 2016.0 13 2021.0 12 2018.0 12 2017.0 11 2015.0 4 2014.0 3 2005.0 1 2013.0 1 2012.0 1 2022.0 1 Name: Started in, dtype: int64
print(shark_tank['Industry'].value_counts())
tmp = shark_tank['Industry'].value_counts().sort_values(ascending=True)
fig = px.bar(tmp, x="Industry", title="<b> Shark Tank India </b>",color = "Industry", template='simple_white', text=tmp)
fig.show()
Food 37 Beauty/Fashion 22 Manufacturing 17 Technology 11 Education 8 Services 7 Medical 7 Electrical Vehicles 4 Animal/Pets 3 Hardware 3 Sports 2 Name: Industry, dtype: int64
print("Total pitchers -", int(shark_tank['Number of Presenters'].sum()), "\n")
print("Total Male pitchers -", int(shark_tank['Male Presenters'].sum()), "\n")
print("Total female pitchers -", int(shark_tank['Female Presenters'].sum()), "\n")
print("Male entrepreneurs percentage - ", round(shark_tank['Male Presenters'].sum()/shark_tank['Number of Presenters'].sum()*100,2), "%\n", sep='')
print("Female entrepreneurs percentage - ", round(shark_tank['Female Presenters'].sum()/shark_tank['Number of Presenters'].sum()*100,2), "%\n", sep='')
print("Couple entrepreneurs percentage - ", round(shark_tank.loc[shark_tank['Couple Presenters']==1]['Couple Presenters'].sum()/shark_tank['Number of Presenters'].sum()*100,2), "% (data incomplete)\n", sep='')
Total pitchers - 252 Total Male pitchers - 177 Total female pitchers - 75 Male entrepreneurs percentage - 70.24% Female entrepreneurs percentage - 29.76% Couple entrepreneurs percentage - 9.92% (data incomplete)
print(shark_tank.groupby('Startup Name')['Yearly Revenue'].max().nlargest(10))
tmpdf = shark_tank.sort_values('Yearly Revenue', ascending=False)[0:10]
fig = px.bar(tmpdf, x="Startup Name", y='Yearly Revenue', color="Startup Name", title="Highest Revenue of the pitches", text=tmpdf['Yearly Revenue'].map(int).map(str) + "%")
fig.show()
Startup Name French Crown 7200.0 Guardian Gears 2500.0 Raising Superstars 1300.0 PlayBoxTV 1020.0 Alpino 1000.0 Hammer Lifestyle 1000.0 Shades of Spring 900.0 Tagz Foods 700.0 Devnagri 500.0 Moonshine 372.0 Name: Yearly Revenue, dtype: float64
print(shark_tank.groupby('Startup Name')['Gross Margin'].max().nlargest(10))
tmpdf = shark_tank.sort_values('Gross Margin', ascending=False)[0:10]
fig = px.bar(tmpdf, x="Startup Name", y='Gross Margin', color="Startup Name", title="Highest Gross margin of the brands", text=tmpdf['Gross Margin'].map(int).map(str) + "%")
fig.show()
Startup Name Poo-de-Cologne 150.0 Farda 115.0 Cocofit 95.0 Auli 80.0 Cos IQ 75.0 Thea and Sid 75.0 Bummer 70.0 French Crown 70.0 Moonshine 70.0 Nomad Food Project 70.0 Name: Gross Margin, dtype: float64
print(round(shark_tank['Received Offer'].value_counts(normalize=True)*100).astype(str).str.replace('.0', '%'), "\n")
sns.countplot(x='Received Offer', data=shark_tank, palette='deep')
1 73% 0 27% Name: Received Offer, dtype: object
<AxesSubplot:xlabel='Received Offer', ylabel='count'>
73% companies received investments while 27% startups could not get offers.
print(round(shark_tank['Accepted Offer'].value_counts(normalize=True)*100).astype(str).str.replace('.0', '%'), "\n")
sns.countplot(x='Accepted Offer', data=shark_tank, palette="viridis")
1.0 76% 0.0 24% Name: Accepted Offer, dtype: object
<AxesSubplot:xlabel='Accepted Offer', ylabel='count'>
76% Companies accepted investments they got and 24% startups did not accepted the offers.
shark_tank.loc[shark_tank['Accepted Offer']==0, ["Startup Name","Original Ask Amount","Original Ask Equity","Valuation Requested","Valuation Offered"]]
| Startup Name | Original Ask Amount | Original Ask Equity | Valuation Requested | Valuation Offered | |
|---|---|---|---|---|---|
| 6 | qZense Labs | 100.0 | 0.25 | 40000 | NaN |
| 19 | Torch-it | 75.0 | 1.00 | 7500 | NaN |
| 20 | La Kheer Deli | 50.0 | 7.50 | 667 | NaN |
| 26 | Kabira Handmad | 100.0 | 5.00 | 2000 | NaN |
| 40 | Morriko Pure Foods | 100.0 | 3.00 | 3333 | NaN |
| 54 | India Hemp and Co | 50.0 | 4.00 | 1250 | NaN |
| 59 | Keto India | 150.0 | 1.25 | 12000 | NaN |
| 69 | Moonshine | 80.0 | 0.50 | 16000 | NaN |
| 70 | Falhari | 50.0 | 2.00 | 2500 | NaN |
| 72 | Urban Monkey | 100.0 | 1.00 | 10000 | NaN |
| 73 | Guardian Gears | 30.0 | 5.00 | 600 | NaN |
| 80 | Alpino | 150.0 | 2.00 | 7500 | NaN |
| 86 | Aliste Technologies | 60.0 | 5.00 | 1200 | NaN |
| 92 | PDD Falcon | 75.0 | 3.00 | 2500 | NaN |
| 93 | PlayBoxTV | 100.0 | 3.50 | 2857 | NaN |
| 103 | ExperentialEtc | 200.0 | 4.00 | 5000 | NaN |
| 105 | C3 Med-Tech | 35.0 | 6.00 | 583 | NaN |
| 112 | Green Protein | 60.0 | 2.00 | 3000 | NaN |
| 115 | Woloo | 50.0 | 4.00 | 1250 | NaN |
| 118 | French Crown | 150.0 | 0.33 | 45455 | NaN |
| 120 | Devnagri | 100.0 | 1.00 | 10000 | NaN |
#Top 14 investments as per total deal/investment amount
print(shark_tank.groupby('Startup Name')['Total Deal Amount'].max().nlargest(14))
tmpdf = shark_tank.sort_values('Total Deal Amount', ascending=False)[0:14]
fig = px.bar(tmpdf, x="Startup Name", y='Total Deal Amount', color="Startup Name", title="Highest Investment as per deal amount (in lakhs)", text=tmpdf['Total Deal Amount'].map(int).map(str) + " lakhs")
fig.show()
Startup Name Aas Vidyalaya 150.0 Annie 105.0 Get-A-Whey 100.0 Hammer Lifestyle 100.0 Humpy A2 100.0 IN A CAN 100.0 Insurance Samadhan 100.0 Proxgy 100.0 Raising Superstars 100.0 Revamp Moto 100.0 Skippi Ice Pops 100.0 Sunfox Technologies 100.0 The Renal Project 100.0 The Yarn Bazaar 100.0 Name: Total Deal Amount, dtype: float64
# Maximum Investment - as per Investment Amount
print("Aman invested -", max(shark_tank['Ashneer Investment Amount'].sum()/100, shark_tank['Namita Investment Amount'].sum()/100, shark_tank['Anupam Investment Amount'].sum()/100, shark_tank['Vineeta Investment Amount'].sum()/100,
shark_tank['Aman Investment Amount'].sum()/100, shark_tank['Peyush Investment Amount'].sum()/100, shark_tank['Ghazal Investment Amount'].sum()/100), "crores")
Aman invested - 8.94490016 crores
# Minimum Investment - as per Investment Amount
print("Ghazal invested -", min(shark_tank['Ashneer Investment Amount'].sum()/100, shark_tank['Namita Investment Amount'].sum()/100, shark_tank['Anupam Investment Amount'].sum()/100, shark_tank['Vineeta Investment Amount'].sum()/100,
shark_tank['Aman Investment Amount'].sum()/100, shark_tank['Peyush Investment Amount'].sum()/100, shark_tank['Ghazal Investment Amount'].sum()/100), "crores")
Ghazal invested - 1.299902525 crores
# Ashneer Grover's Investment
print(shark_tank[shark_tank['Ashneer Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False))
shark_tank[shark_tank['Ashneer Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.show()
tmpdf = shark_tank.loc[shark_tank['Ashneer Investment Amount']>0] [["Startup Name","Ashneer Investment Amount","Ashneer Investment Equity"]].sort_values(by="Ashneer Investment Equity")
fig = px.bar(tmpdf, x="Ashneer Investment Equity", y='Ashneer Investment Amount', color="Startup Name", title="<b>Total equity received by Ashneer (in %) for investment (in lakhs)</b>", text=tmpdf['Ashneer Investment Amount'].map(int).map(str) + " lakhs")
fig.update_layout(dict(xaxis = dict(type="category")))
fig.show()
Food 8 Electrical Vehicles 3 Education 3 Manufacturing 2 Beauty/Fashion 2 Animal/Pets 1 Sports 1 Technology 1 Name: Industry, dtype: int64
# Namita Thapar's Investment
print(shark_tank[shark_tank['Namita Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False))
shark_tank[shark_tank['Namita Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.show()
tmpdf = shark_tank.loc[shark_tank['Namita Investment Amount']>0] [["Startup Name","Namita Investment Amount","Namita Investment Equity"]].sort_values(by="Namita Investment Equity")
fig = px.bar(tmpdf, x="Namita Investment Equity", y='Namita Investment Amount', color="Startup Name", title="<b>Total equity received by Namita (in %) for investment (in lakhs)</b>", text=tmpdf['Namita Investment Amount'].map(int).map(str) + " lakhs")
fig.update_layout(dict(xaxis = dict(type="category")))
fig.show()
Beauty/Fashion 7 Food 6 Education 3 Medical 3 Manufacturing 2 Animal/Pets 1 Sports 1 Services 1 Name: Industry, dtype: int64
# Vineeta singh's Investments
print(shark_tank[shark_tank['Vineeta Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False))
shark_tank[shark_tank['Vineeta Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.show()
tmpdf = shark_tank.loc[shark_tank['Vineeta Investment Amount']>0] [["Startup Name","Vineeta Investment Amount","Vineeta Investment Equity"]].sort_values(by="Vineeta Investment Equity")
fig = px.bar(tmpdf, x="Vineeta Investment Equity", y='Vineeta Investment Amount', color="Startup Name", title="<b>Total equity received by Vineeta (in %) for investment (in lakhs)</b>", text=tmpdf['Vineeta Investment Amount'].map(int).map(str) + " lakhs")
fig.update_layout(dict(xaxis = dict(type="category")))
fig.show()
Food 8 Beauty/Fashion 4 Electrical Vehicles 1 Medical 1 Manufacturing 1 Sports 1 Name: Industry, dtype: int64
# Peyush Bansal's Investments
print(shark_tank[shark_tank['Peyush Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False))
shark_tank[shark_tank['Peyush Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.show()
tmpdf = shark_tank.loc[shark_tank['Peyush Investment Amount']>0] [["Startup Name","Peyush Investment Amount","Peyush Investment Equity"]].sort_values(by="Peyush Investment Equity")
fig = px.bar(tmpdf, x="Peyush Investment Equity", y='Peyush Investment Amount', color="Startup Name", title="<b>Total equity received by Peyush (in %) for investment (in lakhs)</b>", text=tmpdf['Peyush Investment Amount'].map(int).map(str) + " lakhs")
fig.update_layout(dict(xaxis = dict(type="category")))
fig.show()
Manufacturing 4 Beauty/Fashion 4 Food 4 Technology 4 Medical 3 Education 3 Hardware 2 Services 2 Animal/Pets 1 Sports 1 Name: Industry, dtype: int64
# Anupam Mittal's Investments
print(shark_tank[shark_tank['Anupam Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False))
shark_tank[shark_tank['Anupam Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.show()
tmpdf = shark_tank.loc[shark_tank['Anupam Investment Amount']>0] [["Startup Name","Anupam Investment Amount","Anupam Investment Equity"]].sort_values(by="Anupam Investment Equity")
fig = px.bar(tmpdf, x="Anupam Investment Equity", y='Anupam Investment Amount', color="Startup Name", title="<b>Total equity received by Anupam (in %) for investment (in lakhs)</b>", text=tmpdf['Anupam Investment Amount'].map(int).map(str) + " lakhs")
fig.update_layout(dict(xaxis = dict(type="category")))
fig.show()
Beauty/Fashion 6 Food 6 Manufacturing 4 Medical 3 Electrical Vehicles 1 Technology 1 Education 1 Animal/Pets 1 Sports 1 Name: Industry, dtype: int64
# Aman Gupta's Investments
print(shark_tank[shark_tank['Aman Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False))
shark_tank[shark_tank['Aman Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.show()
tmpdf = shark_tank.loc[shark_tank['Aman Investment Amount']>0] [["Startup Name","Aman Investment Amount","Aman Investment Equity"]].sort_values(by="Aman Investment Equity")
fig = px.bar(tmpdf, x="Aman Investment Equity", y='Aman Investment Amount', color="Startup Name", title="<b>Total equity received by Aman (in %) for investment (in lakhs)</b>", text=tmpdf['Aman Investment Amount'].map(int).map(str) + " lakhs")
fig.update_layout(dict(xaxis = dict(type="category")))
fig.show()
Food 11 Beauty/Fashion 6 Manufacturing 4 Education 2 Technology 2 Medical 2 Electrical Vehicles 1 Animal/Pets 1 Name: Industry, dtype: int64
# Ghazal Alagh's Investments
print(shark_tank[shark_tank['Ghazal Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False))
shark_tank[shark_tank['Ghazal Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.show()
tmpdf = shark_tank.loc[shark_tank['Ghazal Investment Amount']>0] [["Startup Name","Ghazal Investment Amount","Ghazal Investment Equity"]].sort_values(by="Ghazal Investment Equity")
fig = px.bar(tmpdf, x="Ghazal Investment Equity", y='Ghazal Investment Amount', color="Startup Name", title="<b>Total equity received by Ghazal (in %) for investment (in lakhs)</b>", text=tmpdf['Ghazal Investment Amount'].map(int).map(str) + " lakhs")
fig.update_layout(dict(xaxis = dict(type="category")))
fig.show()
Food 3 Medical 2 Beauty/Fashion 1 Manufacturing 1 Name: Industry, dtype: int64
print(round(shark_tank['Number of sharks in deal'].value_counts(normalize=True)*100).astype(str).str.replace('.0', '%'))
ax = sns.countplot(data = shark_tank, x = 'Number of sharks in deal')
print('The deals where more than or equals to 5 sharks are involved')
print(shark_tank.loc[shark_tank['Number of sharks in deal'] >= 5][["Startup Name","Total Deal Amount","Total Deal Equity"]])
for i in ax.containers:
ax.bar_label(i,)
1.0 33%
2.0 31%
3.0 22%
4.0 7%
5.0 6%
Name: Number of sharks in deal, dtype: object
The deals where more than or equals to 5 sharks are involved
Startup Name Total Deal Amount Total Deal Equity
15 Skippi Ice Pops 100.0 15.0
49 Find Your Kicks India 50.0 25.0
63 IN A CAN 100.0 10.0
79 Sunfox Technologies 100.0 6.0
print(format_currency(shark_tank['Original Ask Amount'].sum()/100,'INR',locale='en_IN').replace('.00',''),'crores')
₹378.23 crores
print(format_currency(shark_tank['Total Deal Amount'].sum()/100,'INR',locale='en_IN').replace('.00',''),'crores')
₹39.03 crores
Amount = [shark_tank['Ashneer Investment Amount'].sum(), shark_tank['Namita Investment Amount'].sum(), shark_tank['Anupam Investment Amount'].sum(), shark_tank['Vineeta Investment Amount'].sum(),
shark_tank['Aman Investment Amount'].sum(), shark_tank['Peyush Investment Amount'].sum(), shark_tank['Ghazal Investment Amount'].sum()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush','Ghazal']
df = {'Name':name, 'Total Amount':Amount }
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Amount'])
plt.xticks(fontsize=15)
plt.ylabel("Total Investment Amount (in lakhs)",fontsize=15)
for index,d in enumerate(Amount):
plt.text(x=index, y =d+2, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Investment Amount by Sharks", fontsize=15)
plt.show()
# Peyush Bansal's Portfolio
print("Peyush Bansal's total investments", shark_tank[shark_tank['Peyush Investment Amount']>0][['Peyush Investment Amount']].count().to_string()[-2:])
print("Investment amount by Peyush", round(shark_tank['Peyush Investment Amount'].sum()/100, 2), "crores\n")
print("Equity received by Peyush", round(shark_tank['Peyush Investment Equity'].sum(), 2), "% in different companies\n")
print("Debt/loan amount by Peyush", round(shark_tank['Peyush Debt Amount'].sum()/100, 2), "crores\n")
print('-'*80)
print(shark_tank.loc[shark_tank['Peyush Investment Amount']>0][["Startup Name","Industry","Peyush Investment Amount"]].reset_index())
Peyush Bansal's total investments 28
Investment amount by Peyush 7.9 crores
Equity received by Peyush 317.84 % in different companies
Debt/loan amount by Peyush 1.17 crores
--------------------------------------------------------------------------------
index Startup Name Industry Peyush Investment Amount
0 22 Vivalyf Innovations Medical 28.000000
1 25 Ariro Manufacturing 25.000000
2 27 Nuutjob Beauty/Fashion 8.330000
3 28 Meatyour Food 10.000000
4 29 EventBeep Education 10.000000
5 35 LOKA Technology 13.330000
6 36 Annie Education 35.000000
7 37 Carragreen Manufacturing 25.000000
8 38 The Yarn Bazaar Manufacturing 25.000000
9 43 PNT Technology 25.000000
10 49 Find Your Kicks India Beauty/Fashion 10.000000
11 50 Aas Vidyalaya Education 50.000000
12 52 RoadBounce Technology 80.000000
13 58 WeSTOCK Animal/Pets 15.000000
14 61 The State Plate Food 40.000000
15 63 IN A CAN Food 20.000000
16 65 Sid07 Designs Hardware 25.000000
17 67 Hair Originals Beauty/Fashion 20.000000
18 76 KG Agrotech Hardware 10.000000
19 79 Sunfox Technologies Medical 20.000000
20 81 Isak Fragrances Beauty/Fashion 50.000000
21 85 Watt Technovations Medical 0.000253
22 87 Insurance Samadhan Services 100.000000
23 88 Humpy A2 Food 33.330000
24 90 Gold Safe Solutions Manufacturing 16.660000
25 108 Tweek Labs Sports 20.000000
26 109 Proxgy Technology 50.000000
27 119 Store My Goods Services 25.000000
# Aman Gupta's Portfolio
print("Aman Gupta's total investments", shark_tank[shark_tank['Aman Investment Amount']>0][['Aman Investment Amount']].count().to_string()[-2:])
print("Investment amount by Aman", round(shark_tank['Aman Investment Amount'].sum()/100, 2), "crores\n")
print("Equity received by Aman", round(shark_tank['Aman Investment Equity'].sum(), 2), "% in different companies\n")
print("Debt/loan amount by Aman", round(shark_tank['Aman Debt Amount'].sum()/100, 2), "crores\n")
print('-'*80)
print(shark_tank.loc[shark_tank['Aman Investment Amount']>0][["Startup Name","Industry","Aman Investment Amount"]].reset_index())
Aman Gupta's total investments 29
Investment amount by Aman 8.94 crores
Equity received by Aman 164.25 % in different companies
Debt/loan amount by Aman 0.5 crores
--------------------------------------------------------------------------------
index Startup Name Industry Aman Investment Amount
0 0 BluePine Foods Food 25.000000
1 7 Peeschute Beauty/Fashion 75.000000
2 11 Bummer Beauty/Fashion 37.500000
3 12 Revamp Moto Electrical Vehicles 50.000000
4 15 Skippi Ice Pops Food 20.000000
5 18 Raising Superstars Education 50.000000
6 21 Beyond Snack Food 25.000000
7 24 Altor Manufacturing 25.000000
8 25 Ariro Manufacturing 25.000000
9 27 Nuutjob Beauty/Fashion 8.330000
10 28 Meatyour Food 10.000000
11 29 EventBeep Education 10.000000
12 32 Farda Beauty/Fashion 15.000000
13 35 LOKA Technology 13.330000
14 38 The Yarn Bazaar Manufacturing 25.000000
15 39 The Renal Project Medical 50.000000
16 42 Hammer Lifestyle Manufacturing 100.000000
17 44 Cocofit Food 0.000016
18 47 Beyond Water Food 37.500000
19 48 Let's Try Food 22.500000
20 49 Find Your Kicks India Beauty/Fashion 10.000000
21 58 WeSTOCK Animal/Pets 15.000000
22 63 IN A CAN Food 20.000000
23 64 Get-A-Whey Food 33.330000
24 71 Namhya Foods Food 50.000000
25 100 AyuRythm Medical 75.000000
26 104 GrowFitter Technology 50.000000
27 114 Jain Shikanji Food 10.000000
28 117 SneaKare Beauty/Fashion 7.000000
# Ghazal Alagh's Portfolio
print("Ghazal Alagh's total investments", shark_tank[shark_tank['Ghazal Investment Amount']>0][['Ghazal Investment Amount']].count().to_string()[-2:])
print("Investment amount by Ghazal", round(shark_tank['Ghazal Investment Amount'].sum()/100, 2), "crores\n")
print("Equity received by Ghazal", round(shark_tank['Ghazal Investment Equity'].sum(), 2), "% in different companies\n")
print("Debt/loan amount by Ghazal", round(shark_tank['Ghazal Debt Amount'].sum()/100, 2), "crores\n")
print('-'*80)
print(shark_tank.loc[shark_tank['Ghazal Investment Amount']>0][["Startup Name","Industry","Ghazal Investment Amount"]])
Ghazal Alagh's total investments 7
Investment amount by Ghazal 1.3 crores
Equity received by Ghazal 46.7 % in different companies
Debt/loan amount by Ghazal 0.0 crores
--------------------------------------------------------------------------------
Startup Name Industry Ghazal Investment Amount
75 The Sass Bar Beauty/Fashion 25.000000
79 Sunfox Technologies Medical 20.000000
85 Watt Technovations Medical 0.000253
88 Humpy A2 Food 33.330000
90 Gold Safe Solutions Manufacturing 16.660000
91 Wakao Foods Food 25.000000
110 Nomad Food Project Food 10.000000
# Asgneer Grover's Portfolio
print("Ashneer Grover's total investments", shark_tank[shark_tank['Ashneer Investment Amount']>0][['Ashneer Investment Amount']].count().to_string()[-2:])
print("Investment amount by Ashneer", round(shark_tank['Ashneer Investment Amount'].sum()/100, 2), "crores\n")
print("Equity received by Ashneer", round(shark_tank['Ashneer Investment Equity'].sum(), 2), "% in different companies\n")
print("Debt/loan amount by Ashneer", round(shark_tank['Ashneer Debt Amount'].sum()/100, 2), "crores\n")
print('-'*80)
print(shark_tank.loc[shark_tank['Ashneer Investment Amount']>0][["Startup Name","Industry","Ashneer Investment Amount"]])
Ashneer Grover's total investments 21
Investment amount by Ashneer 5.39 crores
Equity received by Ashneer 93.24 % in different companies
Debt/loan amount by Ashneer 1.14 crores
--------------------------------------------------------------------------------
Startup Name Industry Ashneer Investment Amount
0 BluePine Foods Food 25.00
1 Booz Scooters Electrical Vehicles 20.00
3 Tagz Foods Food 70.00
15 Skippi Ice Pops Food 20.00
18 Raising Superstars Education 50.00
21 Beyond Snack Food 25.00
23 Motion Breeze Electrical Vehicles 30.00
29 EventBeep Education 10.00
38 The Yarn Bazaar Manufacturing 25.00
45 Bamboo India Manufacturing 25.00
49 Find Your Kicks India Beauty/Fashion 10.00
50 Aas Vidyalaya Education 50.00
55 Otua Electrical Vehicles 1.00
58 WeSTOCK Animal/Pets 15.00
63 IN A CAN Food 20.00
64 Get-A-Whey Food 33.33
67 Hair Originals Beauty/Fashion 20.00
108 Tweek Labs Sports 20.00
109 Proxgy Technology 50.00
110 Nomad Food Project Food 10.00
114 Jain Shikanji Food 10.00
# Nmita Thapar's Portfolio
print("Namita Thapar's Total Investments", shark_tank[shark_tank['Namita Investment Amount']>0][['Namita Investment Amount']].count().to_string()[-2:])
print("Investment amount by Namita", round(shark_tank['Namita Investment Amount'].sum()/100, 2), "crores\n")
print("Equity received by Namita", round(shark_tank['Namita Investment Equity'].sum(), 2), "% in different companies\n")
print("Debt/loan amount by Namita", round(shark_tank['Namita Debt Amount'].sum()/100, 2), "crores\n")
print('-'*80)
print(shark_tank.loc[shark_tank['Namita Investment Amount']>0][["Startup Name","Industry","Namita Investment Amount"]])
Namita Thapar's Total Investments 24
Investment amount by Namita 6.8 crores
Equity received by Namita 140.78 % in different companies
Debt/loan amount by Namita 0.25 crores
--------------------------------------------------------------------------------
Startup Name Industry Namita Investment Amount
11 Bummer Beauty/Fashion 37.500000
15 Skippi Ice Pops Food 20.000000
16 Menstrupedia Education 50.000000
24 Altor Manufacturing 25.000000
27 Nuutjob Beauty/Fashion 8.330000
32 Farda Beauty/Fashion 15.000000
33 Auli Beauty/Fashion 75.000000
36 Annie Education 35.000000
39 The Renal Project Medical 50.000000
44 Cocofit Food 0.000016
47 Beyond Water Food 37.500000
49 Find Your Kicks India Beauty/Fashion 10.000000
50 Aas Vidyalaya Education 50.000000
58 WeSTOCK Animal/Pets 15.000000
63 IN A CAN Food 20.000000
79 Sunfox Technologies Medical 20.000000
83 Rare Planet Manufacturing 65.000000
85 Watt Technovations Medical 0.000253
91 Wakao Foods Food 25.000000
95 Kabaddi Adda Sports 40.000000
106 Colour Me Mad - CMM Beauty/Fashion 40.000000
110 Nomad Food Project Food 10.000000
117 SneaKare Beauty/Fashion 7.000000
119 Store My Goods Services 25.000000
# Anupam Mittal's Portfolio
print("Anupam Mittal's total investments", shark_tank[shark_tank['Anupam Investment Amount']>0][['Anupam Investment Amount']].count().to_string()[-2:])
print("Investment amount by Anupam", round(shark_tank['Anupam Investment Amount'].sum()/100, 2), "crores\n")
print("Equity received by Anupam", round(shark_tank['Anupam Investment Equity'].sum(), 2), "% in different companies\n")
print("Debt/loan amount by Anupam", round(shark_tank['Anupam Debt Amount'].sum()/100, 2), "crores\n")
print('-'*80)
print(shark_tank.loc[shark_tank['Anupam Investment Amount']>0][["Startup Name","Industry","Anupam Investment Amount"]])
Anupam Mittal's total investments 24
Investment amount by Anupam 5.34 crores
Equity received by Anupam 166.35 % in different companies
Debt/loan amount by Anupam 0.15 crores
--------------------------------------------------------------------------------
Startup Name Industry Anupam Investment Amount
2 Heart up my Sleeves Beauty/Fashion 12.500000
9 Cos IQ Beauty/Fashion 25.000000
12 Revamp Moto Electrical Vehicles 50.000000
15 Skippi Ice Pops Food 20.000000
22 Vivalyf Innovations Medical 28.000000
28 Meatyour Food 10.000000
31 ARRCOAT Surface Textures Manufacturing 50.000000
35 LOKA Technology 13.330000
36 Annie Education 35.000000
37 Carragreen Manufacturing 25.000000
38 The Yarn Bazaar Manufacturing 25.000000
44 Cocofit Food 0.000016
45 Bamboo India Manufacturing 25.000000
48 Let's Try Food 22.500000
49 Find Your Kicks India Beauty/Fashion 10.000000
63 IN A CAN Food 20.000000
66 The Quirky Naari Beauty/Fashion 17.500000
67 Hair Originals Beauty/Fashion 20.000000
75 The Sass Bar Beauty/Fashion 25.000000
78 PawsIndia Animal/Pets 50.000000
79 Sunfox Technologies Medical 20.000000
85 Watt Technovations Medical 0.000253
108 Tweek Labs Sports 20.000000
114 Jain Shikanji Food 10.000000
# Vineeta Singh's Portfolio
print("Vineeta Singh's total investments", shark_tank[shark_tank['Vineeta Investment Amount']>0][['Vineeta Investment Amount']].count().to_string()[-2:])
print("Investment amount by Vineeta", round(shark_tank['Vineeta Investment Amount'].sum()/100, 2), "crores\n")
print("Equity received by Vineeta", round(shark_tank['Vineeta Investment Equity'].sum(), 2), "% in different companies\n")
print("Debt/loan amount by Vineeta", round(shark_tank['Vineeta Debt Amount'].sum()/100, 2), "crores\n")
print('-'*80)
print(shark_tank.loc[shark_tank['Vineeta Investment Amount']>0][["Startup Name","Industry","Vineeta Investment Amount"]])
Vineeta Singh's total investments 16
Investment amount by Vineeta 3.35 crores
Equity received by Vineeta 135.53 % in different companies
Debt/loan amount by Vineeta 0.3 crores
--------------------------------------------------------------------------------
Startup Name Industry Vineeta Investment Amount
0 BluePine Foods Food 25.00
1 Booz Scooters Electrical Vehicles 20.00
2 Heart up my Sleeves Beauty/Fashion 12.50
8 NOCD Food 20.00
9 Cos IQ Beauty/Fashion 25.00
15 Skippi Ice Pops Food 20.00
64 Get-A-Whey Food 33.33
66 The Quirky Naari Beauty/Fashion 17.50
79 Sunfox Technologies Medical 20.00
88 Humpy A2 Food 33.33
90 Gold Safe Solutions Manufacturing 16.66
91 Wakao Foods Food 25.00
95 Kabaddi Adda Sports 40.00
110 Nomad Food Project Food 10.00
114 Jain Shikanji Food 10.00
117 SneaKare Beauty/Fashion 7.00
print(shark_tank.groupby('Startup Name')['Total Deal Equity'].max().nlargest(15))
df1 = shark_tank.sort_values('Total Deal Equity', ascending=False)[0:15]
fig = px.bar(df1, x="Startup Name", y='Total Deal Equity', color="Startup Name", title="Highest Investment as per Equity percentage", text=df1['Total Deal Equity'].map(int).map(str)+'%')
fig.show()
Startup Name Sid07 Designs 75.00 Booz Scooters 50.00 Isak Fragrances 50.00 Hammer Lifestyle 40.00 KG Agrotech 40.00 The Sass Bar 35.00 Vivalyf Innovations 33.33 Gold Safe Solutions 30.00 Heart up my Sleeves 30.00 Jain Shikanji 30.00 Colour Me Mad - CMM 25.00 Cos IQ 25.00 Find Your Kicks India 25.00 PNT 25.00 LOKA 24.00 Name: Total Deal Equity, dtype: float64
print(shark_tank.groupby('Startup Name')['Total Deal Debt'].max().nlargest(15))
df1 = shark_tank.sort_values('Total Deal Debt', ascending=False)[0:15]
fig = px.bar(df1, x="Startup Name", y='Total Deal Debt', color="Startup Name", title="Highest Investment as per loan amount", text=df1['Total Deal Debt'])
fig.show()
Startup Name Otua 99.0 Namhya Foods 50.0 Store My Goods 50.0 Bamboo India 30.0 NOCD 30.0 PNT 25.0 The State Plate 25.0 Sid07 Designs 22.0 KG Agrotech 20.0 ARRCOAT Surface Textures NaN Aas Vidyalaya NaN Agri tourism NaN Aliste Technologies NaN Alpino NaN Altor NaN Name: Total Deal Debt, dtype: float64
Equity = [shark_tank['Ashneer Investment Equity'].sum(), shark_tank['Namita Investment Equity'].sum(), shark_tank['Anupam Investment Equity'].sum(), shark_tank['Vineeta Investment Equity'].sum(),
shark_tank['Aman Investment Equity'].sum(), shark_tank['Peyush Investment Equity'].sum(), shark_tank['Ghazal Investment Equity'].sum()]
df = {'Name':name, 'Total Equity':Equity }
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Equity'])
plt.xticks(fontsize=15)
plt.ylabel("Total Equity (in %)",fontsize=14)
for index,d in enumerate(Equity):
plt.text(x=index, y =d+2, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Equity received by Sharks", fontsize=15)
plt.show()
shark_tank.loc[shark_tank['Valuation Requested'] == shark_tank["Valuation Offered"]][["Startup Name","Valuation Requested","Valuation Offered"]]
| Startup Name | Valuation Requested | Valuation Offered | |
|---|---|---|---|
| 21 | Beyond Snack | 2000 | 2000.0 |
| 44 | Cocofit | 0 | 0.0 |
| 85 | Watt Technovations | 0 | 0.0 |
df2 = shark_tank.loc[shark_tank['Yearly Revenue'] == 0]
print(df2['Startup Name'])
23 Motion Breeze 41 Good Good Piggy 82 Julaa Automation 97 Scholify 99 Sabjikothi 113 On2Cook Name: Startup Name, dtype: object
# Few companies got more amount than they asked/expected
shark_tank.loc[shark_tank['Original Ask Amount'] < shark_tank["Total Deal Amount"]][["Startup Name","Original Ask Amount","Total Deal Amount"]]
| Startup Name | Original Ask Amount | Total Deal Amount | |
|---|---|---|---|
| 0 | BluePine Foods | 50.0 | 75.0 |
| 15 | Skippi Ice Pops | 45.0 | 100.0 |
| 36 | Annie | 30.0 | 105.0 |
| 38 | The Yarn Bazaar | 50.0 | 100.0 |
| 42 | Hammer Lifestyle | 30.0 | 100.0 |
| 58 | WeSTOCK | 50.0 | 60.0 |
| 63 | IN A CAN | 50.0 | 100.0 |
| 75 | The Sass Bar | 40.0 | 50.0 |
| 88 | Humpy A2 | 75.0 | 100.0 |
| 108 | Tweek Labs | 40.0 | 60.0 |
| 109 | Proxgy | 35.0 | 100.0 |
| 117 | SneaKare | 20.0 | 21.0 |
text = " Shark Tank India ".join(cat for cat in shark_tank['Startup Name'])
stop_words = list(STOPWORDS)
wordcloud = WordCloud(width=2000, height=800, stopwords=stop_words, background_color='black', colormap='Set2', collocations=False, random_state=2022).generate(text)
plt.figure(figsize=(25,20))
plt.imshow(wordcloud)
plt.axis("off")
plt.show()